In the pursuit of classifying train stations from Open Railway Maps data, for Europe's largest rail cargo company. Initially, the project focused on developing a robust deep learning framework, which required extensive manual labeling of images to train the model effectively. Recognizing the impracticality and time-consuming nature of manual labeling, we conceptualized an approach to expedite the labeling process using cluster algorithms and graph information.
Our method involved an automated labeling algorithm, which significantly accelerated the annotation phase. This algorithm demonstrated remarkable efficiency, automatically labeling images with high accuracy, thereby drastically reducing the manual effort involved.
During the implementation, we discovered that our automated labeling algorithm was, in itself, the comprehensive solution for the classification task we aimed to address. This realization highlighted that our initial objective of deploying a deep learning model could be achieved through “classic” means.

In conclusion, our project unveiled that the automated labeling algorithm was not just a tool to facilitate deep learning, but an effective standalone solution in itself. This unexpected outcome emphasizing that sometimes, the journey towards deep learning can reveal simpler, yet equally powerful, solutions.

Damn, as all data scientists deep down, we wanted to take advantage of some sexy deep learning and ended up with a great, but not so sexy core data science solution.